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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano
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Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano

机译：结合基于滤波器的特征选择方法和高斯混合模型对科托帕西火山地震事件进行分类

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摘要

This paper proposes an exhaustive evaluation of five different filter-based feature selectionmethods in combination with a Gaussian mixture model classifier for the classification of long-period (LP) and volcano-tectonic (VT) seismic events recorded at Cotopaxi volcano in Ecuador. The experimentation included both exploring and ranking search spaces of seismic-signal-based features, and selecting subsets of optimal features for event classification. The evaluation was carried out by using an experimental dataset formed by 587 LP and 81 VT feature vectors, each composed of 84 statistical, temporal, spectral, and scale-domain features extracted from the original seismic signals. The best result in accuracy, precision, recall, and processing time for LP seismic event classification was obtained by using the Chi2 discretization method with five features, achieving 95.62%, 99.08%, 95.94%, and 3.7 ms, respectively, whereas for VT seismic event classification, the uFilter method with five features reached the scores of 96.71%, 85.23%, 96.00%, and 4.1 ms, respectively. For the classification of both seismic events simultaneously, the uFilter method with five features yielded 96.70%, 97.77%, 96.7%, and 4.1 ms, respectively. According to the Wilcoxon statistical test, these classification schemes provide competitive seismic event classification, while reducing the required processing time.
机译：本文结合高斯混合模型分类器，对五种不同的基于过滤器的特征选择方法进行了详尽的评估，以对厄瓜多尔科托帕希火山记录的长时期（LP）和火山构造（VT）地震事件进行分类。实验包括探索和排序基于地震信号的特征的搜索空间，以及为事件分类选择最佳特征的子集。通过使用由587个LP和81个VT特征向量组成的实验数据集进行评估，每个向量由从原始地震信号中提取的84个统计，时间，频谱和比例域特征组成。通过使用具有五个特征的Chi2离散化方法，在LP地震事件分类的准确性，准确性，召回率和处理时间方面取得了最佳结果，而VT地震分别达到了95.62％，99.08％，95.94％和3.7 ms。在事件分类中，具有五个功能的uFilter方法分别达到96.71％，85.23％，96.00％和4.1 ms的得分。为了同时对两个地震事件进行分类，具有五个特征的uFilter方法分别产生了96.70％，97.77％，96.7％和4.1 ms的时间。根据Wilcoxon统计测试，这些分类方案提供了具有竞争力的地震事件分类，同时减少了所需的处理时间。

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